@@ -30,20 +30,32 @@ More precisely, 🤗 Diffusers offers:
...
@@ -30,20 +30,32 @@ More precisely, 🤗 Diffusers offers:
**Models**: Neural network that models $p_\theta(\mathbf{x}_{t-1}|\mathbf{x}_t)$ (see image below) and is trained end-to-end to *denoise* a noisy input to an image.
**Models**: Neural network that models $p_\theta(\mathbf{x}_{t-1}|\mathbf{x}_t)$ (see image below) and is trained end-to-end to *denoise* a noisy input to an image.
*Examples*: UNet, Conditioned UNet, 3D UNet, Transformer UNet
*Examples*: UNet, Conditioned UNet, 3D UNet, Transformer UNet
<em> Figure from ImageGen (https://imagen.research.google/). </em>
<p>
## Philosophy
## Philosophy
- Readability and clarity is prefered over highly optimized code. A strong importance is put on providing readable, intuitive and elementary code design. *E.g.*, the provided [schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers) are separated from the provided [models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models) and provide well-commented code that can be read alongside the original paper.
- Readability and clarity is prefered over highly optimized code. A strong importance is put on providing readable, intuitive and elementary code design. *E.g.*, the provided [schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers) are separated from the provided [models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models) and provide well-commented code that can be read alongside the original paper.
[Diffuser](https://diffusion-planning.github.io/) for planning in reinforcement learning: [](https://colab.research.google.com/drive/1TmBmlYeKUZSkUZoJqfBmaicVTKx6nN1R?usp=sharing)
### 2. `diffusers` as a collection of popular Diffusion systems (GLIDE, Dalle, ...)
### 2. `diffusers` as a collection of popular Diffusion systems (GLIDE, Dalle, ...)
For more examples see [pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines).
For more examples see [pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines).
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# Denoising Diffusion Implicit Models (DDIM)
## Overview
DDPM was proposed in [Denoising Diffusion Implicit Models](https://arxiv.org/abs/2010.02502) by *Jiaming Song, Chenlin Meng, Stefano Ermon*
The abstract from the paper is the following:
*Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample. To accelerate sampling, we present denoising diffusion implicit models (DDIMs), a more efficient class of iterative implicit probabilistic models with the same training procedure as DDPMs. In DDPMs, the generative process is defined as the reverse of a Markovian diffusion process. We construct a class of non-Markovian diffusion processes that lead to the same training objective, but whose reverse process can be much faster to sample from. We empirically demonstrate that DDIMs can produce high quality samples 10× to 50× faster in terms of wall-clock time compared to DDPMs, allow us to trade off computation for sample quality, and can perform semantically meaningful image interpolation directly in the latent space.*
Tips:
- ...
- ...
This model was contributed by [???](https://huggingface.co/???). The original code can be found [here](https://github.com/hojonathanho/diffusion).
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# Denoising Diffusion Probabilistic Models (DDPM)
## Overview
DDPM was proposed in [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) by *Jonathan Ho, Ajay Jain, Pieter Abbeel*.
The abstract from the paper is the following:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. Our implementation is available at this https URL*
Tips:
- ...
- ...
This model was contributed by [???](https://huggingface.co/???). The original code can be found [here](https://github.com/hojonathanho/diffusion).
